{"title":"Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry","authors":"Al Ansor Siahaan, M. Asrol","doi":"10.24867/ijiem-2023-2-329","DOIUrl":null,"url":null,"abstract":"[1] D. Askenaizer and M. Watson Engineers, “Drinking Water Quality and Treatment,” 2001. [2] S. Sulistyani, A. Fillaeli, U. Negeri, and Y. K. Malang, “Uji kesadahan air tanah di daerah sekitar pantai kecamatan rembang propinsi jawa tengah,” 2012. [3] H. Elfil and A. Hannachi, “Reconsidering water scaling tendency assessment,” AIChE Journal, vol. 52, no. 10, pp. 3583–3591, Oct. 2006, doi: 10.1002/aic.10965. [4] A. Sharjeel, S. Anwar, A. Nasir, and H. Rashid, “Design, development and performance of optimum water softener,” Earth Sciences Pakistan, vol. 3, no. 1, pp. 23–28, Jan. 2019, doi: 10.26480/esp.01.2019.23.28. [5] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4. KeAi Publishing Communications Ltd., pp. 379–391, Dec. 01, 2021. doi: 10.1016/j. ptlrs.2021.05.009. [6] J. Jawad, A. H. Hawari, and S. Zaidi, “Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux,” Desalination, vol. 484, Jun. 2020, doi: 10.1016/j.desal.2020.114427. [7] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, and S. Kumar, “Prediction of groundwater quality using efficient machine learning technique,” Chemosphere, vol. 276, Aug. 2021, doi: 10.1016/j.chemosphere.2021.130265. [8] A. Bannoud, “The electrochemical way of removing the hardness of water,” 1993. [9] A. Mahvi, N. Dariush, V. Forugh, and S. Nazmara, “Teawaste as An Adsorbent for Heavy Metal Removal from Industrial Wastewaters,” Am. J. Appl. Sci., vol. 2, Jan. 2005, doi: 10.3844/ajassp.2005.372.375. [10] C. C. Aggarwal, Neural Networks and Deep Learning. Springer International Publishing, 2018. doi: 10.1007/978-3-319-94463-0. [11] P. Goyal, S. Pandey, and K. Jain, “Unfolding Recurrent Neural Networks,” in Deep Learning for Natural Language Processing, Apress, 2018, pp. 119–168. doi: 10.1007/978-1-4842-3685-7_3. [12] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208. [13] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766. [14] N. K. Manaswi, “RNN and LSTM BT Deep Learning with Applications Using Python : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras,” N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115–126, doi: 10.1007/978-14842-3516-4_9. [15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: 10.1016/j.physd.2019.132306. [16] H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainability (Switzerland), vol. 10, no. 10, Oct. 2018, doi: 10.3390/su10103765. [17] R. Couronné, P. Probst, and A. L. Boulesteix, “Random forest versus logistic regression: A large-scale benchmark experiment,” BMC Bioinformatics, vol. 19, no. 1, Jul. 2018, doi: 10.1186/s12859-018-2264-5. [18] L. Breiman, “Random Forests,” 2001. [19] G. Shmueli, P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl Jr., Data mining for business analytics: concepts, techniques, and applications in R. Wiley, 2017. [20] D. A. Lind, W. G. Marchal, and S. A. Wathen, Statistical techniques in business & economics. McGraw-Hill, 2017. References Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Industrial Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24867/ijiem-2023-2-329","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
[1] D. Askenaizer and M. Watson Engineers, “Drinking Water Quality and Treatment,” 2001. [2] S. Sulistyani, A. Fillaeli, U. Negeri, and Y. K. Malang, “Uji kesadahan air tanah di daerah sekitar pantai kecamatan rembang propinsi jawa tengah,” 2012. [3] H. Elfil and A. Hannachi, “Reconsidering water scaling tendency assessment,” AIChE Journal, vol. 52, no. 10, pp. 3583–3591, Oct. 2006, doi: 10.1002/aic.10965. [4] A. Sharjeel, S. Anwar, A. Nasir, and H. Rashid, “Design, development and performance of optimum water softener,” Earth Sciences Pakistan, vol. 3, no. 1, pp. 23–28, Jan. 2019, doi: 10.26480/esp.01.2019.23.28. [5] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4. KeAi Publishing Communications Ltd., pp. 379–391, Dec. 01, 2021. doi: 10.1016/j. ptlrs.2021.05.009. [6] J. Jawad, A. H. Hawari, and S. Zaidi, “Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux,” Desalination, vol. 484, Jun. 2020, doi: 10.1016/j.desal.2020.114427. [7] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, and S. Kumar, “Prediction of groundwater quality using efficient machine learning technique,” Chemosphere, vol. 276, Aug. 2021, doi: 10.1016/j.chemosphere.2021.130265. [8] A. Bannoud, “The electrochemical way of removing the hardness of water,” 1993. [9] A. Mahvi, N. Dariush, V. Forugh, and S. Nazmara, “Teawaste as An Adsorbent for Heavy Metal Removal from Industrial Wastewaters,” Am. J. Appl. Sci., vol. 2, Jan. 2005, doi: 10.3844/ajassp.2005.372.375. [10] C. C. Aggarwal, Neural Networks and Deep Learning. Springer International Publishing, 2018. doi: 10.1007/978-3-319-94463-0. [11] P. Goyal, S. Pandey, and K. Jain, “Unfolding Recurrent Neural Networks,” in Deep Learning for Natural Language Processing, Apress, 2018, pp. 119–168. doi: 10.1007/978-1-4842-3685-7_3. [12] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208. [13] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766. [14] N. K. Manaswi, “RNN and LSTM BT Deep Learning with Applications Using Python : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras,” N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115–126, doi: 10.1007/978-14842-3516-4_9. [15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: 10.1016/j.physd.2019.132306. [16] H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainability (Switzerland), vol. 10, no. 10, Oct. 2018, doi: 10.3390/su10103765. [17] R. Couronné, P. Probst, and A. L. Boulesteix, “Random forest versus logistic regression: A large-scale benchmark experiment,” BMC Bioinformatics, vol. 19, no. 1, Jul. 2018, doi: 10.1186/s12859-018-2264-5. [18] L. Breiman, “Random Forests,” 2001. [19] G. Shmueli, P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl Jr., Data mining for business analytics: concepts, techniques, and applications in R. Wiley, 2017. [20] D. A. Lind, W. G. Marchal, and S. A. Wathen, Statistical techniques in business & economics. McGraw-Hill, 2017. References Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry
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期刊介绍:
International Journal of Industrial Engineering and Management (IJIEM) is an interdisciplinary international academic journal published quarterly. IJIEM serves researchers in the industrial engineering, manufacturing engineering and management fields. The major aims are: To collect and disseminate information on new and advanced developments in the field of industrial engineering and management; To encourage further progress in engineering and management methodology and applications; To cover the range of engineering and management development and usage in their use of managerial policies and strategies. Thus, IJIEM invites the submission of original, high quality, theoretical and application-oriented research; general surveys and critical reviews; educational or training articles including case studies, in the field of industrial engineering and management. The journal covers all aspects of industrial engineering and management, particularly: -Smart Manufacturing & Industry 4.0, -Production Systems, -Service Engineering, -Automation, Robotics and Mechatronics, -Information and Communication Systems, -ICT for Collaborative Manufacturing, -Quality, Maintenance and Logistics, -Safety and Reliability, -Organization and Human Resources, -Engineering Management, -Entrepreneurship and Innovation, -Project Management, -Marketing and Commerce, -Investment, Finance and Accounting, -Insurance Engineering and Management, -Media Engineering and Management, -Education and Practices in Industrial Engineering and Management.